In the Guardian Datastore, I found a spreadsheet outlining the number of endangered species in each country in 2008 and 2009. The document also subdivided the species into groups of mammals, reptiles, birds and so on.

Usually, I start data experiments with a question and then look for the appropriate data. But in this case, the dataset was so interesting that the questions came naturally: What groups of species were the most at risk of extinction? Where did they live? Had the number of threatened species increased from 2008 to 2009?

I couldn’t even hazard a guess at patterns in species or continental trends by looking at the dataset because the countries were displayed in alphabetical order. I soon realised I would have to call upon various visualisation tools to find answers to my questions.

First of all, using Tableau Public, I created a world map with circles over each country – the smaller the circle, the fewer the endangered species.

This visualisation helped me locate the countries where there was a major threat to wildlife: Ecuador, the USA, Malaysia, Indonesia, China and Mexico.

I was no closer to analysing continental trends, though. So I created another map using the same information, this time with OpenHeatMaps.

Instead of marking each country with circles, OpenHeatMaps filled in the countries with varying shades of one colour according to the number of endangered species living there.

The continental patterns were suddenly easy to spot:

a dark blue strip ran from the United States to Brazil via Mexico and Ecuador

nearly all of South-East Asia was dark blue, from China and India to Australia:

I now wanted to find out whether the number of endangered species had increased in 2009. Again using Tableau Public, I created a scatter chart where the blue dots represented the numbers in 2008 and the orange ones the facts in 2009.

I could easily compare the height of the dots and establish whether the number of endangered species had increased from one year to the next. For most countries, the two dots were superimposed but in some cases, the 2008 dot was , to my surprise, higher than the 2009 one. So some regions had seen a decrease in the number of endangered species within a year.

It shows that plants were the most threatened group in 2009 with 11,025 endangered species. Molluscs, on the other hand, were the least threatened with ‘just’ 1,144 endangered species.

Lastly, I wanted to find out which countries or regions each wildlife group was most at risk in. I achieved this by creating a map where the constant value was the country and the wildlife groups were the variables. I used Tableau Public‘s excellent drag and drop tool to chop and change between variables

Some very intriguing trends appeared:

Amphibians are almost exclusively endangered in Central America

273 species of molluscs are threatened in the United States but next to none are endangered in neighbouring Canada and Mexico

the only wildlife group which is notably under threat in Europe is the fish group

mammals are more endangered in Malaysia than anywhere else

I realise I could have obtained these figures from the initial dataset but by actually seeing them on the map, it was much easier to pick up and analyse regional trends. For example, water pollution in Europe’s rivers is probably to blame for the endangerment of hundreds of fish species.

If you find a great dataset and choose your graphics tools carefully, you can ask draw so many interesting conclusions from your visualisations.